Optimal Strategies for Matching and Retrieval Problems by Comparing Covariates

TL;DR

This paper analyzes optimal retrieval strategies using covariates when covariate recognition is imperfect, covering various matching scenarios and validating formulas through experiments.

Contribution

It provides analytical formulas for optimal retrieval strategies based on covariates with recognition errors, applicable to multiple matching problems.

Findings

Derived formulas for optimal matching with covariate recognition errors

Validated analytical results through experiments

Applicable to large collection retrieval scenarios

Abstract

In many retrieval problems, where we must retrieve one or more entries from a gallery in response to a probe, it is common practice to learn to do by directly comparing the probe and gallery entries to one another. In many situations the gallery and probe have common covariates -- external variables that are common to both. In principle it is possible to perform the retrieval based merely on these covariates. The process, however, becomes gated by our ability to recognize the covariates for the probe and gallery entries correctly. In this paper we analyze optimal strategies for retrieval based only on matching covariates, when the recognition of the covariates is itself inaccurate. We investigate multiple problems: recovering one item from a gallery of $N$ entries, matching pairs of instances, and retrieval from large collections. We verify our analytical formulae through experiments to verify their correctness in practical settings.